def test_qm9(self, run_config, builder_config): job = Regression({ "name": "test", "run_config": run_config, "builder_config": builder_config }) job.run()
def test_cosine_basis(self, run_config, builder_config): job = Regression({ "name": "test", "run_config": run_config, "builder_config": dict(**builder_config, basis_type="cosine"), }) job.run()
def test_sum_points(self, run_config, builder_config): job = Regression({ "name": "test", "run_config": run_config, "builder_config": dict(**builder_config, sum_points=True), }) job.run()
def test_non_residual(self, run_config, builder_config): job = Regression({ "name": "test", "run_config": run_config, "builder_config": dict(**builder_config, residual=False), }) job.run()
def test_default_loads_eagerly(self, run_config, builder_config, model): run_config["run_eagerly"] = True builder_config["dynamic"] = True job = Regression({ "name": "test", "run_config": run_config, "builder_config": builder_config }) job.run() model = load_model(model) assert True
def test_iso17(self, run_config, builder_config): loader_config = {"loader_type": "iso17_loader"} job = Regression({ "name": "test", "run_config": run_config, "loader_config": loader_config, "builder_config": dict(**builder_config, builder_type="force_builder"), }) job.run()
def test_modified_qm9_vector_prediction_cartesian_output( self, run_config, builder_config): loader_config = { "loader_type": "qm9_loader", "load_kwargs": { "modify_structures": True }, } job = Regression({ "name": "test", "run_config": run_config, "loader_config": loader_config, "builder_config": dict(**builder_config, builder_type="cartesian_builder"), }) job.run()
def test_basic_grid_search(self, run_config): job = GridSearch( job=Regression(exp_config={ "name": "test", "run_config": run_config }), grid=self.GRID_CONFIG, total_models=3, ) job.run()
def test_single_dense_radial(self, run_config, builder_config): job = Regression({ "name": "test", "run_config": run_config, "builder_config": dict( **builder_config, **{ "embedding_units": 32, "model_num_layers": (3, 3, 3), "si_units": 32, "radial_factory": "single_dense", "radial_kwargs": { "num_layers": 1, "units": 64, "activation": "ssp", "kernel_lambda": 0.01, "bias_lambda": 0.01, }, }), }) job.run()
def test_regression_to_structure_prediction_to_cross_validation( self, builder_config, run_config): job = Pipeline(jobs=[ Regression( exp_config={ "run_config": run_config, "loader_config": { "loader_type": "iso17_loader" }, "builder_config": dict(**builder_config, builder_type="force_builder"), }), StructurePrediction( exp_config={ "run_config": run_config, "loader_config": { "loader_type": "qm9_loader", "load_kwargs": { "modify_structures": True }, }, "builder_config": dict(**builder_config, builder_type="cartesian_builder"), }), CrossValidate( exp_config={ "run_config": run_config, "loader_config": { "loader_type": "ts_loader", "splitting": 5 }, "builder_config": dict(**builder_config, builder_type="cartesian_builder"), }), ]) job.run()
job = Pipeline( exp_config={ "name": f"{Path(__file__).parent}", "seed": 1 }, jobs=[ Regression( exp_config={ "name": f"{Path(__file__).parent} QM9", "seed": 1, "run_config": { "epochs": 50, "test": False, }, "loader_config": { "loader_type": "qm9_loader", "splitting": "90:10:0", "map_points": False, "load_kwargs": { "custom_maxz": 36 }, }, "builder_config": { "builder_type": "energy_builder" }, }), CrossValidate( exp_config={ "name": f"{Path(__file__).parent} TS", "seed": 1, "run_config": { "epochs": 1000,